Conference Agenda

In the framework of the DRAGON4 Project, the National Institute of Geophysics and Volcanology of Rome (INGV, Italy) and the Northeastern University of Shenyang (China) collaborate to study the surface movement over several industrial regions in Northeast China. The traditional heavy industrial base of Northeast China, especially in the Benxi-Anshan-Shenyang-Fushun (BASF) region, has played an important role in the economic development of the region, although severe consequences on the local environment are taking place due to the continuous mining activities. Various geohazards, such as subsidence, landslides, ground breakage and building inclinations, have been threatening the safety of local people and the environment for decades. The continuous monitoring of the effects of the mentioned geohazards is thus of great importance for the local population well-being. The main objectives of the study are: to take advantage of the availability of dense remote sensing data sets in order to analyze the geohazards and their environmental impacts in the region; and then forecast when and how these geohazards might occur in the future and provide technical support for disaster prevention and damage reduction.

Time series InSAR, as a general term of a variety of algorithms, is able to analyze the spatial and temporal deformation over large areas. With a single SAR image data stack only deformation along the line-of-sight direction could be analyzed. In this analysis we use time-series InSAR results from multiple stacks (from ascending and descending orbits) to monitor slow motions gravitational deformations.

As an important type of glacier, mountain glaciers are often regarded as sensitive recorders and indicators of global climate change. Additionally, glacier movement, one of the most important features of glaciers, can cause serious natural disasters, such as debris flow and glacial lake outburst floods, that threaten human production and life. Thus, monitoring glacier movement has great significance for predicting glacial flow and hazards. COSMO-SkyMed is a constellation of four X-band high-resolution radar satellites with a minimum revisit period of 12 hours. Based on ascending and descending COSMO-SkyMed data acquired at nearly the same time, the surface velocity of the Yiga Glacier, located in the Jiali County, Tibet, China, is estimated in four directions using an offset tracking technique during the periods of 16 January to 3 February 2017 and 1 February to 19 February 2017. Through the geometrical relationships between the measurements and the SAR images, the least square method is used to retrieve the 3D components of the glacier surface velocity in the eastward, northward and upward directions. Four conclusions can be drawn. First, by applying the offset tracking technique to the intensity information of ascending and descending passes of SAR images and combining the four measurements with different directions, the 3D velocity field of glaciers can be estimated. Second, as a constellation with four radar satellites, COSMO-SkyMed has a short revisit time that can acquire ascending and descending images with very similar time periods. This technique has great potential to validate the true 3D velocity of glaciers using different image pairs mapping the same deformation field. Third, the Yiga Glacier had a stable velocity during the observation period from 16 January to 19 February 2017. The distribution of the glacier surface velocity is related to the elevation change. A maximum velocity of approximately 2.4 m/d is observed in the middle part of the glacier because the steepest slope is located there. With steadily decreasing elevation, the velocity in the upper middle and the lower middle portions of the Yiga Glacier stabilizes at approximately 40 cm/d. Finally, the low RMSE in the non-ice region indicates that the results are reliable.

As the third largest country with coal reserves, but China is the largest coal product and consume country in the word. The goafs formed by underground coal extraction often bring severe damages and geohazards to coal mining areas, characterized by uncertainty, slowly and unpredictable over a relatively long time period after post-mining. Generally speaking, detecting the spatial distribution of surface deformation caused by underground goafs for effectively is the basic work for response the subsidence control and geohazards assessment. Compared to traditional geophysical techniques, the satellite-based imagery geodetic observations, such as Differential SAR Interferometry (InSAR) technique, has been considerated as a powerful tool for potentially large-spatial coverage deformation monitoring of the earth’s surface with an accuracy within centimeters to millimeters. As a typical city which abundant in coal resource and thus developed, Xuzhou city, has been experiencing a large-scale and high-intensity coal mining activities over past more than a century, causing large-area land subsidence even collapse phenomenon.In this study, the Multi-Temporal InSAR analysis techniques that both Persistent Scatterers Interferometry (PSI) and Small BAseline Subset (SBAS) methods is implemented, to investigate and analyze the land subsidence over the Xuzhou region, and to conduct an in-depth assessment about the stability of several interested underground goafs, using 62 SAR imagery acquired by Copernicus’ Sentinel-1A satellite spanning July 2015 until Apir 2018. The maps of annal-average subsidence velocity and displacement time-series were generated. And, the reliability of the monitoring results was cross-verified by comparing the PSI results to the SBAS method and highlight the differences. The MT-InSAR results reveal that there have four significant subsidence areas in the Xuzhou region, mainly locating in Tongshan-Quanshan District, Jiawang District, Fengxian and Peixian Country, the main driving factor of land subsidence is underground coal-mining except Fengxian Country, and the most server subsidence take place in Tongshan-Quanshan District where the maximum subsidence rate about 48.6mm/a, which keep better consistency with coal-mining borde in the spatial pattern. Moreover, aiming to the several typical underground goafs, the subsidence characteristics of their were analyzed, and we found that the trend of subsidence over underground goafs present a remarkable behavior that the stabilize continuously in temporal and the subsidece area is shrinking in spatial.It is self-evident thatdetected displacement time series over underground goafs provide valuable insight into the spatial and temporal evolution of corresponding deformation phenomena in recent years, thus it contribute to offer essential insight to the long-term stability assessment of the subsidence coal-mining induced of the Xuzhou region.

Abstract:For a long time, monitoring of mining subsidence requires a lot of money and time, besides monitoring and prediction can not be effectively integrated.So in this paper, the mining area is monitored by using time series InSar, then the data of experimental results and support vector regression (SVR) are combined to predict the dynamic change of mining subsidence.Finally, the rapid monitoring of mine deformation,integration of monitoring and prediction are realized.Firstly,we use PS-InSar and SBAS-InSar technology to get the subsidence scope and development trend of mining area,then the monitoring results after weighted assessment are used as training ang learning samples of SVR algorithm to establish prediction function;Finally, by using the established prediction function, the rolling prediction is carried out based on the results of regression analysis.To test the proposed method,We taking Xinjiang sulphur gully coal mine as an example,and use 36 Scenes of sentinel-1 imagery from 2015 to 2017 to carry out experimental research and analysis.The result shows that：Time series Insar can well monitor the subsidence scope and development trend of mining area,Further more the result of the prediction error of mining subsidence is also better.The experimental results show the feasibility of the method.

Key words: Time series Insar.;Subsidence prediction; mining subsidence; SVR

The Zhouqu–Wudu segment of the Bailongjiang Basin in Northwest of China with a total area of 8917 km² lies in the middle south of the west wing of Qinling orogen. It is controlled by Qinghai–Tibet tectonic belt and Wudu arc structure, and affected by unlift of the Qinghai–Tibet plateau. This segment is located in the Qinling Mountains, and is surrounded by the Qinghai–Tibet Plateau, the Loess Plateau and the Sichuan Basin as the three major geomorphic units. Because of its geophysical conditions, the Bailongjiang Basin is one of the most severely landslide affected regions in China. More than 2000 medium and large landslides have been reported within the Wudu and Zhouqu segment. In this paper, 50 newly launched Sentinel-1 scenes from November 2014 to September 2016 are gathered, and a preprocessing chain of TOPS with SBAS-InSAR are generated to obtain the time series deformation, the active area within the five typical giant fossil landslides in the study area were detected, the maximum deformation and the average deformation were verified by field investigation and the displacement monitor measurements in the local landslide early warning system.